{
 "cells": [
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": [
    "# CUADS Data Quality Notebook\n",
    "\n",
    "## About CUADS\n",
    "CUADS is a multimodal dataset contained ECG, PPG and GSR physiological signals recorded while participants viewed pre-tagged affective movie clips. After each movie clip, participants completed a SAM survey to evaluate arousal and valence.\n",
    "\n",
    "The dataset can obtained from IEEE DataPort here: [https://ieee-dataport.org/documents/clarkson-university-affective-dataset-cuads](https://ieee-dataport.org/documents/clarkson-university-affective-dataset-cuads)\n",
    "\n",
    "\n",
    "## About this notebook\n",
    "This notebook is used to perform quality assessment of the data contained in CUADS. We evaluate and score each of the three physiological signals, and the SAM survey response data.\n",
    "\n",
    "## Instructions\n",
    "Download and extract the CUADS dataset. In the first cell, update the value `dataset_root` to path to the `cuads` folder in the extracted dataset. This notebook produces several output images. The path to save the output can be set in the `dataset_output` variable, and defaults to `./output`.\n",
    "\n",
    "For help, please reach out to the authors."
   ]
  },
  {
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2025-01-26T14:49:11.573822Z",
     "start_time": "2025-01-26T14:49:11.449720Z"
    }
   },
   "cell_type": "code",
   "source": [
    "import os\n",
    "import matplotlib.pyplot as plt\n",
    "import neurokit2 as nk\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "from sklearn.metrics import accuracy_score, precision_score, recall_score, classification_report, confusion_matrix\n",
    "from statsmodels.stats.inter_rater import fleiss_kappa, aggregate_raters\n",
    "\n",
    "# Path to the extracted dataset\n",
    "dataset_root='./dataset/cuads'\n",
    "\n",
    "# Path to write output images and numpy files\n",
    "dataset_output=f'./output'\n",
    "\n",
    "if not os.path.exists(dataset_root):\n",
    "    print(\"ERROR: No dataset found at {dataset_root}\")\n",
    "\n",
    "if not os.path.exists(dataset_output):\n",
    "    os.mkdir(dataset_output)"
   ],
   "outputs": [],
   "execution_count": 1
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-01-26T14:49:11.577133Z",
     "start_time": "2025-01-26T14:49:11.575844Z"
    }
   },
   "cell_type": "code",
   "source": [
    "Fs=256                      # Physiological signal sampling rate. DO NOT MODIFY.\n",
    "CUADS_NUM_PARTICIPANTS=44   # CUADS enrolled 44 participants.\n",
    "CUADS_NUM_MOVIECLIPS=20     # Participants were each shown 20 movie clips"
   ],
   "outputs": [],
   "execution_count": 2
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": [
    "# ECG Scoring\n",
    "\n",
    "The following cell scores the ECGs using NeuroKit2. NeuroKit2 ontains several methods for cleaning an ECG, listed in the `methods` list. The Zhoe2018 method scores the cleaned ECG, rating them as `unacceptable`, `barely acceptable` or `excellent`\n",
    "\n",
    "For each ecg signal in the dataset, we attempt to find the NeuroKit2 method that results in the highest quality score.\n",
    "\n",
    "__NOTE:__ in the Data Descriptor submitted to IEEE-Data, we only report quality results using the `neurokit` Method. This method applies a 0.5 Hz high-pass butterworth filter (order = 5), followed by powerline filtering (nk defaults to 50 Hz, we specify 60Hz in the call to override). Below, we search all available methods and achieve \"Excellent\" scoring for all 714 samples in the dataset\n"
   ]
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-01-26T14:51:58.863592Z",
     "start_time": "2025-01-26T14:49:11.620403Z"
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   },
   "cell_type": "code",
   "source": [
    "# Map column numbers to ECG Channel Names - only used for logging\n",
    "column_to_channel_name = {15: \"LA-RA\", 16: \"LL-LA\", 17: \"LL-RA\"}\n",
    "\n",
    "# Map Zhao2018 result labels to integer values\n",
    "quality_map = { \"Unacceptable\": 0, \"Barely acceptable\": 1, \"Excellent\": 2 }\n",
    "\n",
    "# List of methods for ECG Cleaning in NeuroKit2\n",
    "methods = [\"neurokit\", \"biosppy\", \"pantompkins1985\", \"hamilton2002\", \"elgendi2010\", \"engzeemod2012\", \"vg\"]\n",
    "\n",
    "# Used to track how many times each method resulted in the best score for each signal\n",
    "bestmethod_counts = {\"neurokit\": 0, \"biosppy\": 0, \"pantompkins1985\": 0, \"hamilton2002\": 0, \"elgendi2010\": 0, \"engzeemod2012\": 0, \"vg\": 0}\n",
    "\n",
    "# Used to track how many ecg signals received each score\n",
    "quality_results = { 0: 0, 1: 0, 2: 0 }\n",
    "\n",
    "# Used to track the best scores by ecg-channel\n",
    "quality_by_channel = {0: { 0: 0, 1: 0, 2: 0 }, 1: { 0: 0, 1: 0, 2: 0 }, 2: { 0: 0, 1: 0, 2: 0 } }\n",
    "\n",
    "for p in range(CUADS_NUM_PARTICIPANTS):\n",
    "    folder = f\"{dataset_root}/CUADS_{(p+1):03}\"\n",
    "    if not os.path.exists(folder):\n",
    "        continue\n",
    "\n",
    "    # Load this participant's responses...\n",
    "    responses = np.loadtxt(f\"{folder}/responses.csv\", delimiter=',', dtype=str, skiprows=1)\n",
    "\n",
    "    for c, response in enumerate(responses):\n",
    "        movie_name = response[0]\n",
    "        segmented_data_filepath = f'{folder}/segmented/{movie_name}_sessiondata.csv'\n",
    "        if not os.path.exists(segmented_data_filepath):\n",
    "            #print(f\"No segmented datafile found: {segmented_data_filepath}\")\n",
    "            continue\n",
    "\n",
    "        segmented_data = np.loadtxt(segmented_data_filepath, delimiter=',', dtype=str, skiprows=1)\n",
    "\n",
    "        ## ECG LA-RA 24-Bit = Column 16\n",
    "        ## ECG LL-LA 24-Bit = Column 17\n",
    "        ## ECG LL-RA 24-Bit = Column 18\n",
    "        for channel in np.arange(15,18):\n",
    "            quality = \"x\"\n",
    "            channel_dat=segmented_data[:,channel]\n",
    "            channel_dat = np.array(channel_dat, dtype=np.float64)\n",
    "\n",
    "            methodidx = 0\n",
    "            best_quality = -1\n",
    "            best_quality_method = \"\"\n",
    "\n",
    "            # Loop until we get an \"Excellent\" result, or run out of methods to test...\n",
    "            while quality != \"Excellent\" and methodidx < len(methods):\n",
    "                current_method = methods[methodidx]\n",
    "\n",
    "                # Clean the ECG signal using the current method\n",
    "                ecg_cleaned = nk.ecg_clean(channel_dat, sampling_rate=Fs, powerline=60, method=current_method)\n",
    "\n",
    "                # Evaluate the ECG signal using fuzzy comprehensive SQI evaluation\n",
    "                quality = nk.ecg_quality(ecg_cleaned, sampling_rate=Fs, method='zhao2018', approach=\"fuzzy\")\n",
    "\n",
    "                # Is this better than the best one we've found so far?\n",
    "                if quality_map[quality] > best_quality:\n",
    "                    best_quality = quality_map[quality]\n",
    "                    best_quality_method = current_method\n",
    "\n",
    "                methodidx += 1\n",
    "\n",
    "            quality_by_channel[channel-15][best_quality] += 1\n",
    "            bestmethod_counts[best_quality_method] += 1\n",
    "            quality_results[best_quality] += 1\n",
    "\n",
    "            #print(f\"Participant {p+1} Video {c} Channel {column_to_channel_name[channel]}: Best Method {best_quality_method}, Best Quality: {best_quality}\")\n",
    "\n",
    "print(\"\\nECG Signal Counts by Best Quality (0=Unacceptable, 1=Barely acceptable, 2=Excellent)\")\n",
    "print(quality_results)\n",
    "\n",
    "print(\"\\nECG Signal Counts by Best Method\")\n",
    "print(bestmethod_counts)\n",
    "\n",
    "print(\"\\nECG Signal Counts by By ECG-Channel (0=LA-RA, 1=LL-LA, 2=LL-RA)\")\n",
    "print(quality_by_channel)"
   ],
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "ECG Signal Counts by Best Quality (0=Unacceptable, 1=Barely acceptable, 2=Excellent)\n",
      "{0: 0, 1: 0, 2: 2142}\n",
      "\n",
      "ECG Signal Counts by Best Method\n",
      "{'neurokit': 1824, 'biosppy': 100, 'pantompkins1985': 156, 'hamilton2002': 62, 'elgendi2010': 0, 'engzeemod2012': 0, 'vg': 0}\n",
      "\n",
      "ECG Signal Counts by By ECG-Channel (0=LA-RA, 1=LL-LA, 2=LL-RA)\n",
      "{0: {0: 0, 1: 0, 2: 714}, 1: {0: 0, 1: 0, 2: 714}, 2: {0: 0, 1: 0, 2: 714}}\n"
     ]
    }
   ],
   "execution_count": 3
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": [
    "# PPG Scoring\n",
    "\n",
    "We score PPG by looking at the heartrate inferred from PPG, and computing the euclidean distance to the average heartrate computed across the 3 ECG channels. We then score them on their distance from ECG_HR based on the mean and stdev of the distances.\n",
    "\n",
    "If d < mean - stdev (between 0 and 1 stdev less tham mean), it is excellent\n",
    "if (mean-stdev) <= d <= (mean+stdev), it is good\n",
    "if d > (mean~stdev), it is poor\n",
    "\n",
    "```\n",
    "Let m be the mean distance\n",
    "Let s be the stdev of the distances\n",
    "\n",
    "  |----- EXCELLENT ----|--- GOOD ----|----- POOR -------|\n",
    "  |--------------------|------+------|------------------|\n",
    "min(d)               (m-s)    m    (m+s)              max(d)"
   ]
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-01-26T14:54:30.848018Z",
     "start_time": "2025-01-26T14:51:58.911587Z"
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   },
   "cell_type": "code",
   "source": [
    "hr_distances = []\n",
    "\n",
    "for p in range(CUADS_NUM_PARTICIPANTS):\n",
    "    folder = f\"{dataset_root}/CUADS_{(p+1):03}\"\n",
    "    if not os.path.exists(folder):\n",
    "        continue\n",
    "\n",
    "    # Load this participant's responses...\n",
    "    responses = np.loadtxt(f\"{folder}/responses.csv\", delimiter=',', dtype=str, skiprows=1)\n",
    "\n",
    "    for response in responses:\n",
    "        movie_name = response[0]\n",
    "        segmented_data_filepath = f'{folder}/segmented/{movie_name}_sessiondata.csv'\n",
    "        if not os.path.exists(segmented_data_filepath):\n",
    "            print(f\"No segmented datafile found: {segmented_data_filepath}\")\n",
    "            continue\n",
    "\n",
    "        segmented_data = np.loadtxt(segmented_data_filepath, delimiter=',', dtype=str, skiprows=1)\n",
    "\n",
    "        ## ECG LA-RA HR = Column 20\n",
    "        ## ECG LL-LA HR = Column 21\n",
    "        ## ECG LL-RA HR = Column 22\n",
    "        ## PPG HR       = Column 48\n",
    "\n",
    "        ecg_avg_hr = np.array(segmented_data[:,19:21],dtype=float).mean(axis=1)\n",
    "        ppg_hr = np.array(segmented_data[:,47],dtype=float)\n",
    "        hr_distances.append(np.sqrt(np.sum(np.square(ecg_avg_hr-ppg_hr))))\n",
    "\n",
    "print(\"Heart Rate Stats:\")\n",
    "hr_distances = np.array(hr_distances)\n",
    "mu = np.mean(hr_distances)\n",
    "sigma = np.std(hr_distances)\n",
    "print(f\"Min: {np.min(hr_distances)})\")\n",
    "print(f\"Max: {np.max(hr_distances)}\")\n",
    "print(f\"Stdev: {sigma}\")\n",
    "print(f\"Mean: {mu}\")\n",
    "print(\"\\nScores:\")\n",
    "print(f\"Poor: {len([ d for d in hr_distances if d > (mu+sigma)])}\")\n",
    "print(f\"Good: {len([ d for d in hr_distances if (mu-sigma) <= d <= (mu+sigma)])}\")\n",
    "print(f\"Excellent: {len([ d for d in hr_distances if d < (mu-sigma)])}\")\n",
    "\n"
   ],
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Heart Rate Stats:\n",
      "Min: 172.04682234675633)\n",
      "Max: 15975.490744169096\n",
      "Stdev: 3569.5163257874947\n",
      "Mean: 4812.012427308758\n",
      "\n",
      "Scores:\n",
      "Poor: 128\n",
      "Good: 477\n",
      "Excellent: 109\n"
     ]
    }
   ],
   "execution_count": 4
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": [
    "# GSR Scoring\n",
    "\n",
    "Implemented across the next two cells\n",
    "\n",
    "We score GSR based on RMSE of the Skin Conductance signals. We calculate a reference signal for each participant by averaging skin conductance for all samples from that participant. We then compute the distance from baseline for each signal and rate them as `poor`, `good` or `excellent` based on distance from the mean."
   ]
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-01-26T14:56:51.934071Z",
     "start_time": "2025-01-26T14:54:30.898034Z"
    }
   },
   "cell_type": "code",
   "source": [
    "gsr_signals_by_participant = {}\n",
    "\n",
    "# Load each GSR-SC signal by participant...\n",
    "for p in range(CUADS_NUM_PARTICIPANTS):\n",
    "    folder = f\"{dataset_root}/CUADS_{(p+1):03}\"\n",
    "    if not os.path.exists(folder):\n",
    "        continue\n",
    "\n",
    "    # Load this participant's responses...\n",
    "    responses = np.loadtxt(f\"{folder}/responses.csv\", delimiter=',', dtype=str, skiprows=1)\n",
    "\n",
    "    # Create a list to hold the GSR signals for this participant...\n",
    "    gsr_signals_by_participant[p] = []\n",
    "\n",
    "    for c, response in enumerate(responses):\n",
    "        movie_name = response[0]\n",
    "        segmented_data_filepath = f'{folder}/segmented/{movie_name}_sessiondata.csv'\n",
    "        if not os.path.exists(segmented_data_filepath):\n",
    "            print(f\"No segmented datafile found: {segmented_data_filepath}\")\n",
    "            continue\n",
    "\n",
    "        segmented_data = np.loadtxt(segmented_data_filepath, delimiter=',', dtype=str, skiprows=1)\n",
    "\n",
    "        ## GSR Skin Conductance = Column 37\n",
    "        skin_conductance = np.array(segmented_data[:,36],dtype=float)\n",
    "        gsr_signals_by_participant[p].append(skin_conductance)"
   ],
   "outputs": [],
   "execution_count": 5
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-01-26T14:56:52.023554Z",
     "start_time": "2025-01-26T14:56:51.986404Z"
    }
   },
   "cell_type": "code",
   "source": [
    "rmse_results = {}\n",
    "\n",
    "for participant, gsr_signals in gsr_signals_by_participant.items():\n",
    "    # Determine the maximum length of segmented data files for this participant\n",
    "    max_length = max(len(gsr_signal) for gsr_signal in gsr_signals)\n",
    "\n",
    "    # Pad all segmented data files to the maximum length with zeros\n",
    "    padded_datasets = [np.pad(gsr_signal, (0, max_length - len(gsr_signal)), mode='constant') for gsr_signal in gsr_signals]\n",
    "\n",
    "    # Compute the reference signal (mean across padded datasets)\n",
    "    reference_signal = np.mean(padded_datasets, axis=0)\n",
    "\n",
    "    # Compute normalized RMSE for each segmented data file\n",
    "    rmses = []\n",
    "    for gsr_signal in gsr_signals:\n",
    "        T_i = len(gsr_signal)  # Actual length of the dataset\n",
    "        padded_dataset = np.pad(gsr_signal, (0, max_length - T_i), mode='constant')  # Pad to match reference length\n",
    "        rmse = np.sqrt(np.sum((padded_dataset[:T_i] - reference_signal[:T_i]) ** 2) / T_i)  # Normalize by T_i\n",
    "        rmses.append(rmse)\n",
    "\n",
    "    # Compute thresholds for classification\n",
    "    mu = np.mean(rmses)  # Mean of RMSE values\n",
    "    sigma = np.std(rmses)  # Standard deviation of RMSE values\n",
    "\n",
    "    classifications = []\n",
    "    for rmse in rmses:\n",
    "        if rmse < mu - sigma:\n",
    "            classifications.append(\"Excellent\")\n",
    "        elif mu - sigma <= rmse <= mu + sigma:\n",
    "            classifications.append(\"Good\")\n",
    "        else:\n",
    "            classifications.append(\"Poor\")\n",
    "\n",
    "    # Store results and classifications for the participant\n",
    "    rmse_results[participant] = {\n",
    "        \"RMSE Values\": rmses,\n",
    "        \"Classifications\": classifications\n",
    "    }\n",
    "\n",
    "# Convert results into a DataFrame for easier analysis\n",
    "participant_results = []\n",
    "for participant, results in rmse_results.items():\n",
    "    for i, (rmse, classification) in enumerate(zip(results[\"RMSE Values\"], results[\"Classifications\"])):\n",
    "        participant_results.append({\n",
    "            \"Participant\": participant,\n",
    "            \"Segmented File ID\": i + 1,\n",
    "            \"RMSE\": rmse,\n",
    "            \"Classification\": classification\n",
    "        })\n",
    "\n",
    "# Create a DataFrame from the results\n",
    "results_df = pd.DataFrame(participant_results)\n",
    "\n",
    "# Display the results in tabular format\n",
    "# Count the total number of each classification\n",
    "classification_counts = results_df[\"Classification\"].value_counts()\n",
    "\n",
    "# Display the counts\n",
    "print(\"Total Classifications:\")\n",
    "print(classification_counts)"
   ],
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total Classifications:\n",
      "Classification\n",
      "Good         491\n",
      "Poor         132\n",
      "Excellent     91\n",
      "Name: count, dtype: int64\n"
     ]
    }
   ],
   "execution_count": 6
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": [
    "# Participant Response Evaluation\n",
    "\n",
    "We consider Randolph's Kappa to assess intra-rater agreement of all participants for each movie clip. We expect better than chance agreement and hope to fair-to-moderate agreement based on other studies.\n",
    "\n",
    "We also consider how participants' classification of each movie clip matches the expected classification label (where movies are classified into Quadrants 1, 2, 3 or 4 on the Valence-Arousal space). Confusion matrices are generated to visualize this information."
   ]
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-01-26T14:56:52.034808Z",
     "start_time": "2025-01-26T14:56:52.027645Z"
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   },
   "cell_type": "code",
   "source": [
    "participant_responses_quadrant = np.zeros(shape=(CUADS_NUM_PARTICIPANTS, CUADS_NUM_MOVIECLIPS), dtype=int)\n",
    "expected_responses_quadrant = np.zeros(shape=(CUADS_NUM_MOVIECLIPS), dtype=int)\n",
    "\n",
    "expected_label_map = {\n",
    "    np.str_('video_55'): 2,\n",
    "    np.str_('video_79'): 1,\n",
    "    np.str_('video_111'): 3,\n",
    "    np.str_('video_73'): 2,\n",
    "    np.str_('video_52'): 4,\n",
    "    np.str_('video_146'): 3,\n",
    "    np.str_('video_funny_f'): 1,\n",
    "    np.str_('video_80'): 1,\n",
    "    np.str_('video_cats_f'): 1,\n",
    "    np.str_('video_138'): 3,\n",
    "    np.str_('video_69'): 2,\n",
    "    np.str_('video_dallas_f'): 0,\n",
    "    np.str_('video_detroit_f'): 0,\n",
    "    np.str_('video_53'): 4,\n",
    "    np.str_('video_58'): 4,\n",
    "    np.str_('video_30'): 2,\n",
    "    np.str_('video_earworm_f'): 2,\n",
    "    np.str_('video_newyork_f'): 0,\n",
    "    np.str_('video_90'): 1,\n",
    "    np.str_('video_107'): 2\n",
    "}\n",
    "\n",
    "# Since participants viewed movies in a random order we need\n",
    "# to track what sequence we encounter the movies in during processing,\n",
    "# so that we have consistent indexing later.\n",
    "movie_idx_map={}\n",
    "movie_idx = 0\n",
    "\n",
    "def to_quadrant(a, v):\n",
    "    q = -1\n",
    "    if a >= 5:\n",
    "        if v >= 5:\n",
    "            q = 1\n",
    "        else:\n",
    "            q = 2\n",
    "    else:\n",
    "        if v < 5:\n",
    "            q = 3\n",
    "        else:\n",
    "            q = 4\n",
    "    return q\n",
    "\n",
    "for p in range(CUADS_NUM_PARTICIPANTS):\n",
    "    folder = f\"{dataset_root}/CUADS_{(p+1):03}\"\n",
    "    if not os.path.exists(folder):\n",
    "        continue\n",
    "\n",
    "    responses = np.loadtxt(f\"{folder}/responses.csv\", delimiter=',', dtype=str, skiprows=1)\n",
    "\n",
    "    for c, response in enumerate(responses):\n",
    "        movie_name = response[0]\n",
    "\n",
    "        # If we haven't indexed this movie yet, add it to the movie_idx_map\n",
    "        # and also index its expected response accordingly\n",
    "        if movie_name not in movie_idx_map:\n",
    "            movie_idx_map[movie_name]=movie_idx\n",
    "            expected_responses_quadrant[ movie_idx] = expected_label_map[movie_name]\n",
    "            movie_idx+=1\n",
    "\n",
    "        v=float(response[1])\n",
    "        a=float(response[2])\n",
    "\n",
    "        participant_responses_quadrant[p][movie_idx_map[movie_name]] = to_quadrant(a, v)\n",
    "\n",
    "\n",
    "def convert_quadrant_to_arousal(value):\n",
    "    if value == 0:\n",
    "        return 0\n",
    "    elif value in [1, 2]:\n",
    "        return 1\n",
    "    elif value in [3, 4]:\n",
    "        return -1\n",
    "\n",
    "def convert_quadrant_to_valence(value):\n",
    "    if value == 0:\n",
    "        return 0\n",
    "    elif value in [1, 4]:\n",
    "        return 1\n",
    "    elif value in [2, 3]:\n",
    "        return -1\n",
    "\n",
    "\n",
    "# Apply the conversion\n",
    "participant_responses_arousal = np.vectorize(convert_quadrant_to_arousal)(participant_responses_quadrant)\n",
    "participant_responses_valence = np.vectorize(convert_quadrant_to_valence)(participant_responses_quadrant)\n",
    "expected_responses_arousal = np.vectorize(convert_quadrant_to_arousal)(expected_responses_quadrant)\n",
    "expected_responses_valence = np.vectorize(convert_quadrant_to_valence)(expected_responses_quadrant)"
   ],
   "outputs": [],
   "execution_count": 7
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-01-26T14:56:52.080372Z",
     "start_time": "2025-01-26T14:56:52.076514Z"
    }
   },
   "cell_type": "code",
   "source": [
    "def plot_kappa_scores( kappa_input, title ):\n",
    "    kappa_scores = []\n",
    "    for i in range(kappa_input.shape[0]):\n",
    "        clip_input = kappa_input[i:i + 1, :]  # Extract one row for the current clip\n",
    "        if np.sum(clip_input) > 0:  # Ensure there is at least one valid response\n",
    "            kappa_score = fleiss_kappa(clip_input, method='randolph')\n",
    "            kappa_scores.append(kappa_score)\n",
    "        else:\n",
    "            kappa_scores.append(None)  # Handle empty clips gracefully\n",
    "\n",
    "    ## Create bar chart for Fleiss' Kappa scores\n",
    "    clip_indices = list(range(1, len(kappa_scores) + 1))\n",
    "    data = [score if score is not None else 0 for score in kappa_scores]\n",
    "    plt.figure(figsize=(10, 6))\n",
    "    plt.bar(clip_indices, data, color='skyblue')\n",
    "    plt.xticks(clip_indices)\n",
    "    plt.xlabel(\"Movie Clip\")\n",
    "    plt.ylabel(\"Randolph's Kappa Score\")\n",
    "    plt.title(title)\n",
    "    plt.ylim(np.min(kappa_scores)*-1.1, 1.1*np.max(np.abs(kappa_scores)))  # Fleiss' Kappa ranges from -1 to 1\n",
    "    plt.axhline(0, color='gray', linestyle='--', linewidth=0.8)  # Reference line for 0 agreement\n",
    "    plt.show()\n",
    "\n",
    "\n",
    "def prepare_kappa_input( responses ):\n",
    "    (result, labels) = aggregate_raters(responses.transpose())\n",
    "\n",
    "    zeroidx = np.where(labels == 0)[0]\n",
    "    result = np.delete(result, zeroidx, axis=1)\n",
    "\n",
    "    return result\n",
    "\n",
    "def pad_kappa_inputs_to_n_raters( kappa_input ):\n",
    "    n_raters = int(kappa_input.sum(axis=1).max())\n",
    "    if not np.all(kappa_input.sum(axis=1) == n_raters):\n",
    "        # Normalize all clips to have the same number of ratings\n",
    "        for i in range(kappa_input.shape[0]):\n",
    "            total_responses = int(kappa_input[i].sum())\n",
    "\n",
    "            if total_responses < n_raters:\n",
    "                # Add missing responses proportionally based on existing counts\n",
    "                proportions = kappa_input[i] / total_responses\n",
    "                missing_responses = n_raters - total_responses\n",
    "                additional_responses = np.random.multinomial(missing_responses, proportions)\n",
    "\n",
    "                kappa_input[i] += additional_responses\n",
    "\n",
    "    return kappa_input\n",
    "\n",
    "def process_kappa( participant_responses, response_type ):\n",
    "    # Prepare kappa input based on arousal results\n",
    "    kappa_input = prepare_kappa_input(participant_responses)\n",
    "    kappa_input = pad_kappa_inputs_to_n_raters(kappa_input)\n",
    "    plot_kappa_scores(kappa_input, f\"Randolph's Kappa Agreement Scores Across Movie Clips - {response_type}\")\n",
    "\n",
    "    # Calculate Fleiss' Kappa using only non-neutral movies\n",
    "    kappa_score = fleiss_kappa(kappa_input, method='randolph')\n",
    "    print(\"Randolph's Kappa Score:\", kappa_score)\n",
    "\n",
    "    # Identify indices of non-neutral movies\n",
    "    # Filter the fleiss_input matrix to exclude neutral movies\n",
    "    non_neutral_movie_indices = [i for i, label in enumerate(expected_responses_quadrant) if label != 0]\n",
    "    filtered_kappa_input = kappa_input[non_neutral_movie_indices]\n",
    "    plot_kappa_scores(filtered_kappa_input, f\"Randolph's Kappa Agreement Scores Across Non-Neutral Movie Clips - {response_type}\")\n",
    "\n",
    "    # Calculate Randolph's Kappa using only non-neutral movies\n",
    "    filtered_kappa_score = fleiss_kappa(filtered_kappa_input, method='randolph')\n",
    "    print(\"Randolph's Kappa Score (excluding neutral movies):\", filtered_kappa_score)\n"
   ],
   "outputs": [],
   "execution_count": 8
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-01-26T14:56:52.402012Z",
     "start_time": "2025-01-26T14:56:52.122785Z"
    }
   },
   "cell_type": "code",
   "source": [
    "process_kappa( participant_responses_arousal, \"Arousal\")\n",
    "process_kappa( participant_responses_valence, \"Valence\")\n",
    "process_kappa(participant_responses_quadrant, \"Quadrant\")"
   ],
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Randolph's Kappa Score: 0.06927453769559033\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Randolph's Kappa Score (excluding neutral movies): 0.07455443059158218\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Randolph's Kappa Score: 0.39189189189189166\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Randolph's Kappa Score (excluding neutral movies): 0.4382060078654506\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Randolph's Kappa Score: 0.18245614035087718\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Randolph's Kappa Score (excluding neutral movies): 0.20586840710679724\n"
     ]
    }
   ],
   "execution_count": 9
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-01-26T14:59:03.983684Z",
     "start_time": "2025-01-26T14:59:03.343286Z"
    }
   },
   "cell_type": "code",
   "source": [
    "\n",
    "def stats(y_true, y_pred, classes):\n",
    "    # Compute Accuracy\n",
    "    accuracy = accuracy_score(y_true, y_pred)\n",
    "\n",
    "    # Compute Precision and Recall for Each Class\n",
    "    precision = precision_score(y_true, y_pred, average=None)  # Per-class precision\n",
    "    recall = recall_score(y_true, y_pred, average=None)        # Per-class recall\n",
    "\n",
    "    # Macro and Weighted Averages\n",
    "    macro_precision = precision_score(y_true, y_pred, average='macro')\n",
    "    macro_recall = recall_score(y_true, y_pred, average='macro')\n",
    "\n",
    "    weighted_precision = precision_score(y_true, y_pred, average='weighted')\n",
    "    weighted_recall = recall_score(y_true, y_pred, average='weighted')\n",
    "\n",
    "    # Print Results\n",
    "    print(f\"Accuracy: {accuracy:.2f}\")\n",
    "    print(\"Per-Class Precision:\", precision)\n",
    "    print(\"Per-Class Recall:\", recall)\n",
    "    print(f\"Macro Precision: {macro_precision:.2f}\")\n",
    "    print(f\"Macro Recall: {macro_recall:.2f}\")\n",
    "    print(f\"Weighted Precision: {weighted_precision:.2f}\")\n",
    "    print(f\"Weighted Recall: {weighted_recall:.2f}\")\n",
    "\n",
    "    # Optional: Full Classification Report\n",
    "    print(\"\\nClassification Report:\")\n",
    "    print(classification_report(y_true, y_pred, target_names=classes))\n",
    "\n",
    "\n",
    "def make_matrix(cm_responses, cm_labels, cm_classes, cm_xlabel, cm_ylabel, fname):\n",
    "    non_neutral_movie_indices = [i for i, label in enumerate(cm_labels) if label != 0]\n",
    "    confusion_responses = cm_responses[:, non_neutral_movie_indices]\n",
    "\n",
    "    expected_labels = cm_labels[non_neutral_movie_indices]\n",
    "\n",
    "    valid_responses = confusion_responses.flatten() != 0\n",
    "    y_true = np.tile(expected_labels, confusion_responses.shape[0])[valid_responses]\n",
    "    y_pred = confusion_responses.flatten()[valid_responses]\n",
    "    stats(y_true, y_pred, cm_classes)\n",
    "    # Generate confusion matrix\n",
    "    cm = confusion_matrix(\n",
    "        y_true,\n",
    "        y_pred,\n",
    "        normalize=\"pred\"\n",
    "    )\n",
    "\n",
    "    # Plot the confusion matrix\n",
    "    plt.figure(figsize=(10, 8))\n",
    "    sns.heatmap(cm, annot=True, fmt='.2f', cmap=\"Greys\", xticklabels=cm_classes, yticklabels=cm_classes, annot_kws={\"size\": 18})\n",
    "    plt.xlabel(cm_xlabel, fontsize=12)\n",
    "    plt.ylabel(cm_ylabel, fontsize=12)\n",
    "    plt.xticks(fontsize=14)\n",
    "    plt.yticks(fontsize=14)\n",
    "    plt.savefig(fname, dpi=300, bbox_inches=\"tight\")\n",
    "\n",
    "cm_responses = participant_responses_quadrant\n",
    "cm_labels = expected_responses_quadrant\n",
    "cm_classes = [\"I\", \"II\", \"III\", \"IV\" ]\n",
    "cm_xlabel = \"Expected Rating (Quadrant)\"\n",
    "cm_ylabel = \"Participant Rating (Quadrant)\"\n",
    "make_matrix(\n",
    "    cm_responses,\n",
    "    cm_labels,\n",
    "    cm_classes,\n",
    "    cm_xlabel,\n",
    "    cm_ylabel,\n",
    "    f\"{dataset_output}/quadrant_matrix.png\"\n",
    ")\n",
    "\n",
    "cm_responses = participant_responses_arousal\n",
    "cm_labels = expected_responses_arousal\n",
    "cm_classes = [\"Low\", \"High\" ]\n",
    "cm_xlabel = \"Expected Rating (Arousal)\"\n",
    "cm_ylabel = \"Participant Rating (Arousal)\"\n",
    "make_matrix(\n",
    "    cm_responses,\n",
    "    cm_labels,\n",
    "    cm_classes,\n",
    "    cm_xlabel,\n",
    "    cm_ylabel,\n",
    "    f\"{dataset_output}/arousal_matrix.png\"\n",
    ")\n",
    "\n",
    "cm_responses = participant_responses_valence\n",
    "cm_labels = expected_responses_valence\n",
    "cm_classes = [\"Negative\", \"Positive\" ]\n",
    "cm_xlabel = \"Expected Rating (Valence)\"\n",
    "cm_ylabel = \"Participant Rating (Valence)\"\n",
    "make_matrix(\n",
    "    cm_responses,\n",
    "    cm_labels,\n",
    "    cm_classes,\n",
    "    cm_xlabel,\n",
    "    cm_ylabel,\n",
    "    f\"{dataset_output}/valence_matrix.png\"\n",
    ")\n"
   ],
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.48\n",
      "Per-Class Precision: [0.54716981 0.59770115 0.34745763 0.27884615]\n",
      "Per-Class Recall: [0.63736264 0.48826291 0.38679245 0.27102804]\n",
      "Macro Precision: 0.44\n",
      "Macro Recall: 0.45\n",
      "Weighted Precision: 0.48\n",
      "Weighted Recall: 0.48\n",
      "\n",
      "Classification Report:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           I       0.55      0.64      0.59       182\n",
      "          II       0.60      0.49      0.54       213\n",
      "         III       0.35      0.39      0.37       106\n",
      "          IV       0.28      0.27      0.27       107\n",
      "\n",
      "    accuracy                           0.48       608\n",
      "   macro avg       0.44      0.45      0.44       608\n",
      "weighted avg       0.48      0.48      0.48       608\n",
      "\n",
      "Accuracy: 0.58\n",
      "Per-Class Precision: [0.4009009  0.67875648]\n",
      "Per-Class Recall: [0.41784038 0.66329114]\n",
      "Macro Precision: 0.54\n",
      "Macro Recall: 0.54\n",
      "Weighted Precision: 0.58\n",
      "Weighted Recall: 0.58\n",
      "\n",
      "Classification Report:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         Low       0.40      0.42      0.41       213\n",
      "        High       0.68      0.66      0.67       395\n",
      "\n",
      "    accuracy                           0.58       608\n",
      "   macro avg       0.54      0.54      0.54       608\n",
      "weighted avg       0.58      0.58      0.58       608\n",
      "\n",
      "Accuracy: 0.82\n",
      "Per-Class Precision: [0.85958904 0.78481013]\n",
      "Per-Class Recall: [0.78683386 0.85813149]\n",
      "Macro Precision: 0.82\n",
      "Macro Recall: 0.82\n",
      "Weighted Precision: 0.82\n",
      "Weighted Recall: 0.82\n",
      "\n",
      "Classification Report:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "    Negative       0.86      0.79      0.82       319\n",
      "    Positive       0.78      0.86      0.82       289\n",
      "\n",
      "    accuracy                           0.82       608\n",
      "   macro avg       0.82      0.82      0.82       608\n",
      "weighted avg       0.82      0.82      0.82       608\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 1000x800 with 2 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 1000x800 with 2 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 1000x800 with 2 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "execution_count": 12
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}
